Molecular Mechanisms Underlying Anticancer and Anti-Inflammatory Activities of Oridonin in Oral Squamous Cell Carcinoma
Bibliographic record
Abstract
Background. Oral squamous cell carcinoma (OSCC) constitutes one of the most common pathological forms of oral cancers. Oridonin is an ent-kaurane diterpenoid compound isolated from Rabdosia rubescens. Recently, the anticancer potential of Oridonin has been extensively studied in breast, osteosarcoma, myeloma, neuroblastoma, lymphoma, pancreatic, colon, leukemia, and esophageal cancers. The anticancer potential of Oridonin is largely unexplored in OSCC. Method. This study aimed to provide insights into the multifunctional anticancer activities of Oridonin in OSCC. We carried out an extensive and critical literature survey on research related to the importance of medicinal plants in various cancers, role of Oridonin as potential anticancer agents in OSCC up to 2025 using keywords apoptotic proteins, antitumor activities, cell cycle arrest, diterpenoid, inflammasomes, Notch signaling pathway, natural products, Oridonin, oral squamous cell carcinoma, and oral cancer treatment. Results. Oridonin induces cell apoptosis in oral cancer cells (OCC) by regulating mitochondrial and ROS-mediated JNK/p38 MAPK, acting as cell cycle blocker at the G2/M phase pathways, and increasing the expression of γH2AX. Oridonin plays an essential role in OSCC tumorigenesis by inhibiting the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway. Blocking Notch signaling dysregulation and specific inhibition of NLRP3 inflammasome are the other cellular mechanism by which Oridonin can exhibit its antitumor activities. Conclusion. Oridonin can serve as a potential anticancer drug in OSCC due to its involvement in multiple cellular signaling pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".